Impala users can expect new performance and usability benefits via improved integration with Kudu.
It’s been nearly one year since the public beta announcement of Kudu (now a top-level Apache project) and a noteworthy milestone has been reached: its 1.0 release. This is particularly exciting as Kudu extends the use cases that can be supported on the Apache Hadoop platform, whether it be on-premises or in the cloud,
As a warm-up to Spark Summit West in San Francisco (June 6-8), we’ve added a new project to Cloudera Labs that makes building Spark Streaming pipelines considerably easier.
Spark Streaming is the go-to engine for stream processing in the Cloudera stack. It allows developers to build stream data pipelines that harness the rich Spark API for parallel processing, expressive transformations, fault tolerance, and exactly-once processing. But it requires a programmer to write code,
Thanks to Richard Williamson of Silicon Valley Data Science for allowing us to republish the following post about his sample application based on Apache Spark, Apache Kudu (incubating), and Apache Impala (incubating).
Why should your infrastructure maintain a linear growth pattern when your business scales up and down during the day based on natural human cycles? There is an obvious need to maintain a steady baseline infrastructure to keep the lights on for your business,
Cloudera Engineering has developed (and recently open sourced) a distributed unit testing framework that cuts testing time from multiple hours to just 10 minutes.
Upstream unit tests are Cloudera’s first line of defense for finding and fixing software bugs, as part of a multidimensional process that also includes static/dynamic code analysis, fault injection, integration/scale/endurance testing, and validation on real workloads. However, running a full unit test suite for Apache Hadoop ecosystem components can take hours,
Using Apache Impala (incubating) on top of Apache Kudu (incubating) has significant performance benefits
Apache Kudu (incubating) is the newest addition to the set of storage engines that integrate with the Apache Hadoop ecosystem. The promise of Kudu is to deliver high-scan performance, targeting analytical workloads, while allowing users to concurrently insert, update, and delete records. With these properties, Kudu becomes a viable alternative to existing combinations of HDFS and/or Apache HBase to achieve similar results with less complicated ETL pipelines,